Background: The identification and quantification of actionable mutations are of critical importance for effective genotype-directed therapies, prognosis and drug response monitoring in patients with non-small-cell lung cancer (NSCLC). Although tumor tissue biopsy remains the gold standard for diagnosis of NSCLC, the analysis of plasma circulating tumor DNA (ctDNA), known as liquid biopsy, has recently emerged as an alternative and noninvasive approach for exploring tumor genetic constitution. In this study, we developed a mutation detection approach for liquid biopsy using ultra-deep massively parallel sequencing (MPS) with unique molecular identifier (UID) tagging and evaluated its performance for the identification and quantification of tumor-derived mutations from plasma of patients with advanced NSCLC. Methods: Tissue biopsy and plasma samples were collected from a total of 58 patients diagnosed with NSCLC in Vietnam. Genetic alterations in four driver genes including EGFR, KRAS, NRAS and BRAF were identified by using ultra-deep MPS combined with UID tagging. Subsequently, the concordance rate of mutation testing between matched plasma and tissue samples was assessed. Additionally, a commercially available ddPCR (Bio-rad) assay was used to conduct a cross-platform comparison with ultra-deep MPS for the detection and quantification of the three most common actionable EGFR mutations (del19, L858R and T790M). Results: Compared to the mutations detected in paired tissue samples, the plasma based ultra-deep MPS achieved high concordance rate of 87.5%. Cross-platform comparison with droplet digital PCR demonstrated comparable detection performance (91.4% concordance, Cohen's kappa coefficient of 0.85 with 95% CI = 0.72 – 0.97) and great reliability in quantification of mutation allele frequency (Intraclass correlation coefficient of 0.96 with 95% CI = 0.90 – 0.98). Conclusions: Our results highlight the potential application of liquid biopsy using ultra-deep MPS as a routine assay in clinical practice for both detection and quantification of actionable mutation landscape in NSCLC patients.